Institutional Investor's Alpha Magazine - November 2007 - (Page 70)

“The main complaint people had was that their algorithms were going too slowly,” says John Huth, director of portfolio management at Quantlab Capital Management. who rely on algorithms. Although the architecture was up to the task, trade results differed widely. Problems reﬂected not just the traffic congestion and overall slowdown but also the way traders selected or employed algorithms. Execution algorithms are a standard part of most hedge funds’ trading practices. But relying too heavily on any one algorithm style without having the knowledge or ability to shift to a platform better suited to fast-changing market conditions can invite trouble. Traders need to know the differences between algorithms and the features that make them more or less attractive in differing markets. Traders who chose the wrong algorithms to deal with the summer’s volatility found themselves vulnerable. And in some cases traders who returned to the old-fashioned way of trading — through a broker — wound up with better results. The other family of ﬁnancial algorithms — quantitative models that use mathematical formulas to analyze historical trends and identify trading opportunities — showed deeper cracks. In several instances quant models misinterpreted the importance of the subprime lending crisis and the accompanying liquidity shortage. Hedge funds that were heavily leveraged found themselves forced into costly margin calls. “Everybody trading quant models made lots of assumptions about historical correlations and valuations,” says Gregory van Kipnis, general partner in Tiedemann Investment Group, a New York–based hedge fund firm. “Those models clearly broke down.” Because quant models differ from one hedge fund to another, each fund must solve for itself any associated problems. The models are closely guarded secrets, so fund managers generally aren’t talking about their experiences. But they have been poring over data and ﬁnancial results since August, trying to determine where their formulas worked and where they need adjustment, then backtesting new assumptions. Execution algorithms, on the other hand, face a different set of challenges. More widely used, these algorithms are available through prime brokers and a variety of vendors. Westborough, Massachusetts–based research firm TABB Group estimates that execution algorithms account for roughly 20 percent of all buy-side trades today and will rise to 23 percent within the next few years. Buy-side algorithms, in turn, are a big part of the explosion in electronic execution, which now accounts for 60 percent of trading by all market participants. The first algorithms to be developed were fairly simple, created to break large block trades into several small orders that don’t leave footprints in the market. The goal was to prevent others from spotting big trades and jumping in to buy or sell the same shares, pushing prices up or down to make a quick proﬁt. Thanks partly to the widespread use of these algorithms, the average equity trade size on the NYSE, which stood at 1,500 shares in 1995, dipped to a low of 292 in August, before rising back to more than 300 in September, according to TABB Group. Over the years, as more sophisticated algorithms emerged, brokers began offering their clients multiple choices. Hundreds of algorithms are available today, and most institutional brokers offer about a dozen. Despite the wide array, however, most algorithms are simply variations on a few core themes. Beyond slicing and dicing, more sophisticated algorithms try to time orders to capture market price or volume averages, or they kick in near the open or close of the trading day. Most recently, algorithms that search for dark pools of liquidity (trading among institutional investors not done on the primary exchanges) to execute orders have been gaining popularity. Choosing the right algorithm can be critical during unstable market periods. Basic, timed algorithms with slow, measured execution are not suited for volatile, fastmoving markets. Newer algorithms are increasingly ﬂexible, allowing traders to adjust execution parameters and more subtly tweak how and when orders are executed. Sofianos recently looked at thousands of trades that were executed through the Goldman system to examine how traders responded to the hectic summer market . He had expected that during the most volatile periods, traders would shift away from passive algorithms to morePhoto by Pam Francis for Alpha 70 • INSTITUTIONAL INVESTOR’S ALPHA • NOVEMBER 2007

Table of Contents Feed for the Digital Edition of Institutional Investor's Alpha Magazine - November 2007

Institutional Investor's Alpha Magazine - November 2007 Contents Letter from the Editor Longs & Shorts Pension Corner: Patriot State The Good Guys: College Connection Interview: Currency of Change Cover Story: Comeback Kid Strategies: Masters of Art Research Center: Alpha Awards: Top Technology Providers Top Analysts Alpha Bytes: Critical Choice Unhedged: Commentary: Banking on a Bubble

Institutional Investor's Alpha Magazine - November 2007

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